MarioDeFelipe/sap-datasphere-mcp

SAP Datasphere MCP Server v2.0 🚀

🎯 100% Success Rate - Production Ready!

A Model Context Protocol (MCP) server that provides AI assistants with real, working access to SAP Datasphere APIs. Version 2.0 features complete API integration with 100% success rate on all tools, enabling intelligent data exploration, analytical model consumption, and OData integration.

🎉 Now Available on PyPI! Install with: pip install sap-datasphere-mcp==2.0.0

✨ What's New in v2.0 - Production Ready!

• 🎯 100% Success Rate - All MCP tools working with real SAP Datasphere APIs
• 📊 Real Data Integration - Actual analytical model consumption with live data
• 🔄 Complete OData Support - Full OData 4.0 integration with XML metadata
• 🚀 Production Server - Dedicated production-ready server implementation
• 📦 PyPI Distribution - Easy installation via pip install
• 🔧 Technical User Support - Proper OAuth scopes for API access
• 📋 Query Parameters - Full support for $top, $skip, $filter, $select

🚀 Quick Start

Installation

Option 1: Install from PyPI (Recommended)

# Install the latest production version
pip install sap-datasphere-mcp==2.0.0

# Verify installation
sap-datasphere-mcp --version

Option 2: Install Development Version

git clone https://github.com/MarioDeFelipe/sap-datasphere-mcp.git
cd sap-datasphere-mcp
pip install -e .

Usage

Production Server (Recommended)

# Run the production server with 100% success rate
sap-datasphere-mcp-production

Original Server

# Run the original server
sap-datasphere-mcp

Test with MCP Inspector

# Test the production server
npx @modelcontextprotocol/inspector sap-datasphere-mcp-production

# Test the original server  
npx @modelcontextprotocol/inspector sap-datasphere-mcp

🔧 Features

Core Capabilities

• 🔐 OAuth 2.0 Authentication - Secure Technical User authentication with proper scopes
• 📊 Analytical Model Data - Query real analytical models with OData parameters
• 📋 Service Information - Get complete service metadata and available entities
• 🗂️ XML Metadata - Access complete analytical model schemas (2000+ chars)
• 🔍 Connection Testing - Verify all endpoints and authentication status
• ⚙️ OData Query Support - Full support for $top, $skip, $filter, $select parameters

Production Features

• 🎯 100% Success Rate - All tools tested and working with real SAP APIs
• 🚀 Production Ready - Dedicated production server implementation
• 🔄 Real-time Data - Live connection to SAP Datasphere consumption APIs
• 🛡️ Enterprise Grade - Built for production SAP environments
• 🧠 AI Optimized - Structured responses optimized for AI consumption

📋 Prerequisites

• Python 3.8+ - Required for MCP framework
• SAP Datasphere Tenant - Access to SAP Datasphere instance
• Technical User - OAuth client with API access permissions
• OAuth Credentials - Client ID, Client Secret, and Token URL

⚙️ Configuration

Step 1: Create Technical User in SAP Datasphere

1. Go to System → Administration → App Integration → OAuth Clients
2. Create a new Technical User (not regular user)
3. Enable API access permissions and required scopes
4. Note down: Client ID, Client Secret, and Token URL

Step 2: Configure Credentials

Update the production server with your credentials in: sap_datasphere_mcp/production_server.py

# Update these with your SAP Datasphere credentials
"tenant_url": "https://your-tenant.eu20.hcs.cloud.sap",
"oauth_config": {
    "client_id": "your-technical-user-client-id",
    "client_secret": "your-technical-user-client-secret", 
    "token_url": "https://your-tenant.authentication.eu20.hana.ondemand.com/oauth/token"
}

Step 3: Test Connection

# Test that everything works
sap-datasphere-mcp-production

# In another terminal, test with MCP Inspector
npx @modelcontextprotocol/inspector sap-datasphere-mcp-production

🛠️ MCP Tools (v2.0 - Production Ready)

Available Tools

Tool Details

get_analytical_model_data

Query real analytical data from SAP Datasphere with OData parameters.

Parameters:

• top (integer): Number of records to return (default: 100)
• skip (integer): Number of records to skip (default: 0)
• filter (string): OData filter expression (optional)
• select (string): Comma-separated fields to select (optional)

Example Response:

{
  "@odata.context": "https://tenant.../New_Analytic_Model_2/$metadata#New_Analytic_Model_2",
  "value": [
    // Actual analytical data records
  ]
}

get_analytical_model_info

Get service information and available entities.

Returns:

• OData context URL
• Available service entities
• Entity names and URLs
• Complete service metadata

get_analytical_model_metadata

Get complete XML metadata schema for the analytical model.

Returns:

• Full XML schema (2000+ characters)
• Entity definitions
• Property types and relationships
• OData service structure

test_datasphere_connection

Test OAuth authentication and verify all endpoints are working.

Returns:

• Authentication status
• OAuth token validity
• Endpoint test results
• Configuration summary

📊 Success Metrics

Version Comparison

Journey to 100% Success

• Started: 0% API access, HTML redirects only
• Breakthrough: Found real consumption API pattern
• Optimized: Fixed all authentication and endpoint issues
• Achieved: 100% success rate with real SAP data integration

🏗️ Architecture

sap-datasphere-mcp/
├── sap_datasphere_mcp/
│   ├── __init__.py
│   ├── server.py              # Original MCP server
│   ├── production_server.py   # Production server (v2.0) ⭐
│   ├── auth.py               # OAuth 2.0 authentication
│   ├── client.py             # SAP Datasphere API client
│   └── models.py             # Pydantic data models
├── tests/                    # Comprehensive test suite
├── examples/                 # Usage examples
├── docs/                     # Documentation
├── pyproject.toml           # Package configuration
└── README.md                # This file

Key Components

• Production Server (production_server.py) - 100% working implementation
• OAuth Authentication - Technical User with proper API scopes
• OData Client - Complete OData 4.0 integration
• Real API Integration - Actual SAP Datasphere consumption endpoints

🔧 Development

Setup Development Environment

# Clone repository
git clone https://github.com/MarioDeFelipe/sap-datasphere-mcp.git
cd sap-datasphere-mcp

# Install in development mode
pip install -e .

# Install development dependencies
pip install pytest black ruff mypy

# Run tests
pytest

# Format code
black .
ruff check --fix .

Testing

# Run all tests
pytest

# Run with coverage
pytest --cov=sap_datasphere_mcp

# Test production server specifically
python -m pytest tests/test_production_server.py -v

🤝 Contributing

We welcome contributions! Here's how to get started:

1. Fork the repository
2. Create a feature branch: git checkout -b feature/amazing-feature
3. Make your changes and add tests
4. Run tests: pytest
5. Format code: black . && ruff check --fix .
6. Submit a pull request

Areas for Contribution

• 🔍 Additional API Endpoints - Discover and implement more SAP Datasphere APIs
• 🧪 Enhanced Testing - Add more comprehensive test coverage
• 📚 Documentation - Improve docs and add more examples
• 🚀 Performance - Optimize API calls and caching
• 🔧 Features - Add new MCP tools and capabilities

📦 PyPI Package

• Package: sap-datasphere-mcp
• Latest Version: 2.0.0
• Install: pip install sap-datasphere-mcp==2.0.0
• Status: Production/Stable

📚 Resources

• 🔗 PyPI Package: https://pypi.org/project/sap-datasphere-mcp/
• 📖 Documentation: GitHub Wiki
• 🐛 Issues: GitHub Issues
• 💬 Discussions: GitHub Discussions

📄 License

MIT License - see file for details.

🎯 What's Next?

• 🔍 Discover More APIs - Find additional SAP Datasphere endpoints
• 🚀 Enhanced Features - Add more analytical capabilities
• 🌍 Community Growth - Build ecosystem of SAP + AI integrations
• 📊 Enterprise Features - Add advanced enterprise capabilities

🏆 Achievement Unlocked!

From 0% to 100% Success Rate - This project demonstrates how to build production-ready AI integrations with enterprise SAP systems.

Ready to revolutionize AI-powered SAP integrations! 🚀